Despite the measures put in place in different countries, road traffic fatalities are still considered one of the leading causes of death worldwide. Thus, the reduction of traffic fatalities or accidents is one of the contributing factors to attaining sustainability goals. Different factors such as the geometric structure of the road, a non-signalized road network, the mechanical failure of vehicles, inexperienced drivers, a lack of communication skills, distraction and the visual or cognitive impairment of road users have led to this increase in traffic accidents. These factors can be categorized under four headings that are: human, road, vehicle factors and environmental road conditions. The advent of machine learning algorithms is of great importance in analysing the data, extracting hidden patterns, predicting the severity level of accidents and summarizing the information in a useful format. In this study, three machine learning algorithms for classification, such as Decision Tree, LightGBM and XGBoost, were used to model the accuracy of road traffic accidents in the UK for the year 2020 using their default and hyper-tuning parameters. The results show that the high performance of the Decision Tree algorithm with default parameters can predict traffic accident severity and provide reference to the critical variables that need to be monitored to reduce accidents on the roads. This study suggests that preventative strategies such as regular vehicle technical inspection, traffic policy strengthening and the redesign of vehicle protective equipment be implemented to reduce the severity of road accidents caused by vehicle characteristics.

Background: Finding well-known Beauveria bassiana isolates that could preserve rice crops from Sesamia calamistis (stem borer) is problematic. Another difficult task is the development of precise inoculation methods, which have been employed for their establishment as endophytes in cereal crops. This study proposed machine learning models to predict the best entomopathogenic fungi, Beauveria bassiana that could directly protect rice crops against Sesamia calamistis. Methods: Data driven machine learning decisions were implemented and assessed from 60 experimental runs with nine different feature/input variables and three target/output variables following foliar spray and seed treatment inoculation method. The feature variables consisted of rice plant tissue, such as Nerica-L19, Nerica1, Nerica8, the time, and the five promising isolates Beauveria bassiana (Bb3, Bb4, Bb10, Bb21, Bb35). The target variable consisted of the number of colonised roots, stems and leaves, expressed as a percentage depending on the degree of protection after each inoculation. A data driven decision by the extreme gradient boosting regression algorithm was used to proficiently abstract the situation where there is no direct relationship between features and target variables. Results: The foliar spray inoculation method exhibited high coefficient of determination (R2 ) of 0.99, 0.98 and 0.94 depending on the number of colonised stems, roots and leaves, respectively, while the seed treatment approach exhibited the coefficient of determination (R2 ) of 0.91, 0.87 and 0.75, respectively. Conclusions: These results demonstrated that the Extreme Gradient Boosting algorithm effectively abstracted the nonlinear relationship between the attribute variables that were taken into consideration and predicted Beauveria bassiana as a bio-pesticide for rice and perhaps other cereal stem borers. Thus, this XGBoost regression model could be used to navigate the optimization domain and reduce the development time of the biocontrol process.

This study introduces a hybrid analytical–machine learning framework for solving the Schrödinger equation with complex potentials. The semi‐inverse variational method is first used to generate highly accurate eigenfunctions and eigenenergies for both 1D radial potentials (Yukawa and Cornell) and a 2D coupled anharmonic oscillator. Based on these rigorous, physics‐consistent results, we train supervised machine learning models; including Random Forest and Neural Network regressors; to predict energy eigenvalues across wide parameter ranges. Both models achieve near‐perfect predictive accuracy (R ² > 0.999) with errors of only a few millielectronvolts, while preserving fundamental quantum‐mechanical trends. Feature importance analysis confirms that the quantum number n and potential strength parameters dominate the energy scaling, in agreement with theoretical expectations. By integrating variational physics with data‐driven emulation, this hybrid framework reduces computational cost by orders of magnitude; enabling rapid, high‐throughput exploration of quantum systems across dimensions. The approach not only accelerates parameter screening but also serves as a discovery tool, uncovering emergent scaling laws and critical confinement behavior in mixed potentials. This synergy between analytical rigor and machine learning efficiency opens new pathways for quantum simulation, materials design, and the discovery of novel quantum phenomena.